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Field
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projects utilizing machine learning, deep learning, and generative AI to solve business and healthcare problems have been undertaken at the Insight Lab. For more details, please refer to: https
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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contribute to a programme exploring how experimentation and innovation unfold in public services, using immersive qualitative research methods such as fieldwork, interviews, and facilitated learning sessions
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laser processing and to bring your ideas in AI/ML to the technology level. You have a solid background in programming (deep learning, reinforcement learning, etc.), electronics, high-speed data
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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic
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experience in deep learning frameworks (TensorFlow/PyTorch) Experience with large-scale genomic/proteomic datasets and machine learning applied to biological sequences Knowledge of phylogenetics, protein
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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pathway prediction. Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions, and identify novel synthetic routes. Curate and manage reaction datasets from literature
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., 2019; Pedersini et al., 2023). We combine ophthalmological, neuroimaging and behavioral data, and incorporate deep learning methods to facilitate biomarker discovery and enhance predictive power. As a
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related field. The ideal candidates will have experience in one or more of the following topics: deep learning for image and point cloud data processing, deep learning for time series data prediction